Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

Christine Xia Wu, Ernest Suresh, Francis Wei Loong Phng, Kai Pik Tai, Janthorn Pakdeethai, Jared Louis Andre D'souza, Woan Shin Tan, Phillip Phan, Kelvin Sin Min Lew, Gamaliel Yu Heng Tan, Gerald Seng Wee Chua, Chi Hong Hwang

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: To develop a risk score for the real-time prediction of readmissions for patients using patient specific information captured in electronic medical records (EMR) in Singapore to enable the prospective identification of high-risk patients for enrolment in timely interventions. Methods: Machine-learning models were built to estimate the probability of a patient being readmitted within 30 days of discharge. EMR of 25,472 patients discharged from the medicine department at Ng Teng Fong General Hospital between January 2016 and December 2016 were extracted retrospectively for training and internal validation of the models. We developed and implemented a real-time 30-day readmission risk score generation in the EMR system, which enabled the flagging of high-risk patients to care providers in the hospital. Based on the daily high-risk patient list, the various interfaces and flow sheets in the EMR were configured according to the information needs of the various stakeholders such as the inpatient medical, nursing, case management, emergency department, and postdischarge care teams. Results: Overall, the machine-learning models achieved good performance with area under the receiver operating characteristic ranging from 0.77 to 0.81. The models were used to proactively identify and attend to patients who are at risk of readmission before an actual readmission occurs. This approach successfully reduced the 30-day readmission rate for patients admitted to the medicine department from 11.7% in 2017 to 10.1% in 2019 (p < 0.01) after risk adjustment. Conclusion: Machine-learning models can be deployed in the EMR system to provide real-time forecasts for a more comprehensive outlook in the aspects of decision-making and care provision.

Original languageEnglish (US)
Pages (from-to)372-382
Number of pages11
JournalApplied clinical informatics
Volume12
Issue number2
DOIs
StatePublished - Mar 1 2021

Keywords

  • data modeling
  • electronic health records and systems
  • implementation and deployment
  • machine learning
  • testing and evaluation

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Health Information Management

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